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      <title>18.4.&nbsp;PageRank Algorithm - Chapter&nbsp;18.&nbsp;Deprecated: Graph Algorithms</title>
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               <p class="nav-title hidden">18.4.&nbsp;PageRank Algorithm</p></span><span class="nav-next"><a accesskey="n" href="../centrality/">Centrality Algorithms<span class="fa fa-long-arrow-right" aria-hidden="true"></span></a></span></nav>
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                  <h2 class="title" style="clear: both"><a class="anchor" href="#pagerank"></a>18.4.&nbsp;PageRank Algorithm
                  </h2>
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                     <td class="icon"><i class="fa icon-warning" title="warning"></i></td>
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                        <p>Graph Algorithms (similarity, centrality and clustering) in APOC are deprecated and about to be removed.
                           Please use the algorithms in <a class="link" href="https://r.neo4j.com/algo" target="_top">neo4j-graph-algorithms</a> instead.
                        </p>
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         <section class="section" id="_setup_2">
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                     <h3 class="title"><a class="anchor" href="#_setup_2"></a>18.4.1.&nbsp;Setup
                     </h3>
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            <p>Let&#8217;s create some test data to run the PageRank algorithm on.</p><pre class="programlisting highlight"><code data-lang="cypher">// create 100 nodes
FOREACH (id IN range(0,1000) | CREATE (:Node {id:id}))

// over the cross product (1M) create 100.000 relationships
MATCH (n1:Node),(n2:Node) WITH n1,n2 LIMIT 1000000 WHERE rand() &lt; 0.1

CREATE (n1)-[:TYPE_1]-&gt;(n2)</code></pre></section>
         <section class="section" id="_pagerank_procedure">
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                     <h3 class="title"><a class="anchor" href="#_pagerank_procedure"></a>18.4.2.&nbsp;PageRank Procedure
                     </h3>
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               </div>
            </div>
            <p>PageRank is an algorithm used by Google Search to rank websites in their search engine results.</p>
            <p>It is a way of measuring the importance of nodes in a graph.</p>
            <p>PageRank counts the number and quality of relationships to a node to approximate the importance of that node.</p>
            <p>PageRank assumes that more important nodes likely have more relationships.</p>
            <p>Caution: <code class="literal">nodes</code> specifies the nodes for which a PageRank score will be projected, but the procedure will <span class="emphasis"><em>always</em></span> compute the PageRank algorithm on the <span class="emphasis"><em>entire</em></span> graph. At present, there is no way to filter/reduce the number of elements that PageRank computes over.
            </p>
            <p>A future version of this procedure will provide the option of computing PageRank on a subset of the graph.</p><pre class="programlisting highlight"><code data-lang="cypher">MATCH (node:Node)
WHERE node.id %2 = 0
WITH collect(node) AS nodes
// compute over relationships of all types
CALL apoc.algo.pageRank(nodes) YIELD node, score
RETURN node, score
ORDER BY score DESC</code></pre><pre class="programlisting highlight"><code data-lang="cypher">MATCH (node:Node)
WHERE node.id %2 = 0
WITH collect(node) AS nodes
// only compute over relationships of types TYPE_1 or TYPE_2
CALL apoc.algo.pageRankWithConfig(nodes,{types:'TYPE_1|TYPE_2'}) YIELD node, score
RETURN node, score
ORDER BY score DESC</code></pre><pre class="programlisting highlight"><code data-lang="cypher">MATCH (node:Node)
WHERE node.id %2 = 0
WITH collect(node) AS nodes
// peroform 10 page rank iterations, computing only over relationships of type TYPE_1
CALL apoc.algo.pageRankWithConfig(nodes,{iterations:10,types:'TYPE_1'}) YIELD node, score
RETURN node, score
ORDER BY score DESC</code></pre></section>
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